Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Drowning is a significant public health issue with more than 320,000 deaths globally every year. These numbers are greatly underestimated, however, due to factors such as inadequate data collection, inconsistent categorization and failure to report in certain regions and cultures.The objective of this study was to develop a standardised drowning dictionary using a consensus-based approach. Through creation of this resource, improved clarity amongst stakeholders will be achieved and, as a result, so will our understanding of the drowning issue. METHODOLOGY: A list of terms and their definitions were created and sent to 16 drowning experts with a broad range of backgrounds across four continents and six languages. A review was conducted using a modified Delphi process over five rounds. A sixth round was done by an external panel evaluating the terms' content validity. RESULTS: The drowning dictionary included more than 350 terms. Of these, less than 10% had been previously published in peer review literature. On average, the external expert validity endorsing the dictionary shows a Scale Content Validity Index (S-CVI/Ave) of 0.91, exceeding the scientific recommended value. Ninety one percent of the items present an I-CVI (Level Content Validity Index) value considered acceptable (>0.78). The endorsement was not a universal agreement (S-CVI/UA: 0.44). CONCLUSION: The drowning dictionary provides a common language, and the authors envisage that its use will facilitate collaboration and comparison across prevention sectors, education, research, policy and treatment. The dictionary will be open to readers for discussion and further review at www.idra.world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it